The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems

The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active...

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Main Author:	Atomi, Walid Hasen
Format:	Thesis
Language:	English English
Published:	2012
Subjects:	QA Mathematics QA71-90 Instruments and machines
Online Access:	http://eprints.uthm.edu.my/2156/1/24p%20WALID%20HASEN%20ATOMI.pdf http://eprints.uthm.edu.my/2156/2/WALID%20HASEN%20ATOMI%20WATERMARK.pdf
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id	my-uthm-ep.2156
record_format	uketd_dc
spelling	my-uthm-ep.21562021-10-31T03:16:55Z The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems 2012-12 Atomi, Walid Hasen QA Mathematics QA71-90 Instruments and machines The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active area of research and numerous papers have been reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained with back-propagation artificial neural network (BP-ANN) method is highly influenced by the size of the data-sets and the data-preprocessing techniques used. This work analyzes the advantages of using pre-processing datasets using different techniques in order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results showed that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques. 2012-12 Thesis http://eprints.uthm.edu.my/2156/ http://eprints.uthm.edu.my/2156/1/24p%20WALID%20HASEN%20ATOMI.pdf text en public http://eprints.uthm.edu.my/2156/2/WALID%20HASEN%20ATOMI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English
topic	QA Mathematics QA71-90 Instruments and machines
spellingShingle	QA Mathematics QA71-90 Instruments and machines Atomi, Walid Hasen The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
description	The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active area of research and numerous papers have been reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained with back-propagation artificial neural network (BP-ANN) method is highly influenced by the size of the data-sets and the data-preprocessing techniques used. This work analyzes the advantages of using pre-processing datasets using different techniques in order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results showed that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Atomi, Walid Hasen
author_facet	Atomi, Walid Hasen
author_sort	Atomi, Walid Hasen
title	The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
title_short	The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
title_full	The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
title_fullStr	The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
title_full_unstemmed	The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
title_sort	effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2012
url	http://eprints.uthm.edu.my/2156/1/24p%20WALID%20HASEN%20ATOMI.pdf http://eprints.uthm.edu.my/2156/2/WALID%20HASEN%20ATOMI%20WATERMARK.pdf
_version_	1747830916454023168

The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems

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